G-BLASTN is a GPU-accelerated nucleotide alignment tool based on the widely used NCBI-BLAST. G-BLASTN can produce exactly the same results as NCBI-BLAST, and it also has very similar user commands. It also supports a pipeline mode, which can fully use the GPU and CPU resources when handling a batch of medium to large sized queries. Currently, G_BLASTN supports the blastn and megablast modes of NCBI-BLAST. The discontiguous megablast mode is not supported yet. More information: http://www.comp.hkbu.edu.hk/~chxw/software/G-BLASTN.html
AMD CodeXL is a free set of tools for GPU debugging, GPU profiling, static analysis of OpenCL kernels, and CPU profiling, including support for remote servers. For more information and download links, see: http://developer.amd.com/community/blog/2013/11/08/codexl-1-3-released/
Bolt is an STL compatible C++ template library for creating data-parallel applications using C++ (no C++ AMP / OpenCL code required). For more information about the Bolt template library and download links, see: http://developer.amd.com/tools-and-sdks/heterogeneous-computing/amd-accelerated-parallel-processing-app-sdk/bolt-c-template-library/
AMD APP SDK has everything needed to get started with OpenCL and parallel programming. It includes OpenCL samples that are very easy to compile, as well as the Bolt and other libraries. For more information about AMD APP SDK and download links, see: http://developer.amd.com/tools-and-sdks/heterogeneous-computing/amd-accelerated-parallel-processing-app-sdk/
Allinea DDT is part of Allinea Software’s unified tools platform, which provides a single powerful and intuitive environment for debugging and profiling of parallel and multithreaded applications. It is widely used by computational scientists and scientific programmers to fix software defects of parallel applications running on hybrid GPU clusters and supercomputers. DDT 4.1.1 supports CUDA 5.5, C++11 and the GNU 4.8 compilers. Also introduced with Allinea DDT 4.1.1 is CUDA toolkit debugging support for ARMv7 architectures. More information: http://www.allinea.com
A free, pre-alpha release of Lab4241′s GPGPU profiler is now available at www.lab4241.com. It provides source-code-line performance profiling for C or C++ code and CUDA kernels in a non-intrusive way. The profiler enables the developer to a seamless evaluation of used GPU resources (execution counts, memory access, branch diversions, etc.) per source-line, along with result evaluation in a simple, intuitive GUI, similar as with known CPU profilers like Quantify or valgrind.
rCUDA (remote CUDA) v4.0 has just been released. It provides full binary compatibility with CUDA applications (no need to modify the application source code or recompile your program), native InfiniBand support, enhanced data transfers, and CUDA 5.0 API support (excluding graphics interoperability). This new release of rCUDA allows to execute existing GPU-accelerated applications by leveraging remote GPUs within a cluster (both via sharing and/or aggregating GPUs) with a negligible overhead. The new version is available free of charge ar www.rCUDA.net, along with examples, manuals and additional information.
The CUDA 5 Production Release is now available as a free download at www.nvidia.com/getcuda.
This powerful new version of the pervasive CUDA parallel computing platform and programming model can be used to accelerate more of applications using the following four (and many more) new features.
• CUDA Dynamic Parallelism brings GPU acceleration to new algorithms by enabling GPU threads to directly launch CUDA kernels and call GPU libraries.
• A new device code linker enables developers to link external GPU code and build libraries of GPU functions.
• NVIDIA Nsight Eclipse Edition enables you to develop, debug and optimize CUDA code all in one IDE for Linux and Mac OS.
• GPUDirect Support for RDMA provides direct communication between GPUs in different cluster nodes
As a demonstration of the power of Dynamic Parallelism and device code linking, CUDA 5 includes a device-callable version of the CUBLAS linear algebra library, so threads already running on the GPU can invoke CUBLAS functions on the GPU. Read the rest of this entry »
AMD CodeXL is a new unified developer tool suite that enables developers to harness the benefits of CPUs, GPUs and APUs. It includes powerful GPU debugging, comprehensive GPU and CPU profiling, and static OpenCL™ kernel analysis capabilities, enhancing accessibility for software developers to enter the era of heterogeneous computing. AMD CodeXL is available for free, both as a Visual Studio® extension and a standalone user interface application for Windows® and Linux®.
AMD CodeXL increases developer productivity by helping them identify programming errors and performance issues in their application quickly and easily. Now developers can debug, profile and analyze their applications with a full system-wide view on AMD APU, GPU and CPUs.
AMD CodeXL user group (requires registration) allows users to interact with the CodeXL team, provide feedback, get support and participate in the beta surveys.
SnuCL is an OpenCL framework and freely available, open-source software developed at Seoul National University. It naturally extends the original OpenCL semantics to the heterogeneous cluster environment. The target cluster consists of a single host node and multiple compute nodes. They are connected by an interconnection network, such as Gigabit and InfiniBand switches. The host node contains multiple CPU cores and each compute node consists of multiple CPU cores and multiple GPUs. For such clusters, SnuCL provides an illusion of a single heterogeneous system for the programmer. A GPU or a set of CPU cores becomes an OpenCL compute device. SnuCL allows the application to utilize compute devices in a compute node as if they were in the host node. Thus, with SnuCL, OpenCL applications written for a single heterogeneous system with multiple OpenCL compute devices can run on the cluster without any modifications. SnuCL achieves both high performance and ease of programming in a heterogeneous cluster environment.
SnuCL consists of SnuCL runtime and compiler. The SnuCL compiler is based on the OpenCL C compiler in SNU-SAMSUNG OpenCL framework. Currently, the SnuCL compiler supports x86, ARM, and PowerPC CPUs, AMD GPUs, and NVIDIA GPUs.
VexCL is vector expression template library for OpenCL developed by the Supercomputer Center of Russian academy of sciences. It has been created for ease of C++ based OpenCL development. Multi-device (and multi-platform) computations are supported. The code is publicly available under MIT license.
- Selection and initialization of compute devices according to extensible set of device filters.
- Transparent allocation of device vectors spanning multiple devices.
- Convenient notation for vector arithmetic, sparse matrix-vector multiplication, reductions. All computations are performed in parallel on all selected devices.
- Appropriate kernels for vector expressions are generated automatically first time an expression is used.
Doxygen-generated documentation is available at http://ddemidov.github.com/vexcl/index.html.
Traditional CPU-based computing environments offer a variety of binary instrumentation frameworks. Instrumentation and analysis tools for GPU environments to date have been more limited. Panoptes is a binary instrumentation framework for CUDA that targets the GPU. By exploiting the GPU to run modified kernels, computationally-intensive programs can be run at the native parallelism of the device during analysis. To demonstrate its instrumentation capabilities, we currently implement a memory addressability and validity checker that targets CUDA programs.
Panoptes traces targeted programs by library interposition at runtime. Read the rest of this entry »